For high-volume manufacturing, yield estimation is an important design step to determine the effects of uncertainties in the fabrication process. The tolerances associated with the fabrication process are applied to the statistically significant system parameters, and a Monte Carlo (MC) simulation is historically done to accurately estimate the yield. This process becomes computationally very expensive when the number of statistically significant system parameters are either too difficult to intuitively determine or are too high. A nonlinear partial-least-squares-based polynomial chaos expansion (NLPLSs-based PCE) is proposed as a solution for complex antenna yield analysis. NLPLS-based PCE effectively reduces the system dimensionality using NLPLS and simultaneously extracts the statistical information on the same sample set, that is, yield, using PCE. It is also possible to perform a global sensitivity analysis using NLPLS-based PCE surrogates, providing an additional advantage. This method is illustrated using an eight-variable single-frequency patch antenna, an eight-variable dual-band patch antenna, and a 37-variable diplexer requiring 30, 10, and 30 analysis points, respectively, to obtain converged yield estimates.